Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique: A Tutorial and an Annotated Example

Communications of the Association for Information Systems, vol. 36(11)

32 Pages Posted: 17 Nov 2014 Last revised: 5 Apr 2015

See all articles by Clay Posey

Clay Posey

University of Central Florida

Tom Roberts

University of Kansas - School of Business

Paul Benjamin Lowry

Virginia Tech - Pamplin College of Business

Becky Bennett

Louisiana Tech University - Department of Management and Information Systems

Date Written: September 10, 2014

Abstract

Formative modelling of latent constructs has produced great interest and discussion among scholars in recent years. However, confusion exists surrounding the ability of researchers to validate these models especially with covariance-based structural equation modelling (CB-SEM) techniques. This manuscript helps to clarify these issues and explains how formatively modelled constructs can be assessed rigorously by researchers using CB-SEM capabilities. In particular, we explain and provide an applied example of a mixed-modelling technique termed multiple indicators and multiple causes (MIMIC) models. Using this approach, researchers can assess formatively modelled constructs as the final, distal dependent variable in structural models, which modelling is traditionally impossible due to the mathematical identification rules of CB-SEM. Moreover, we assert that researchers can use MIMIC models to assess the content validity of a set of formative indicators quantitatively — something considered conventionally only from a qualitative standpoint. Our research example used in this manuscript involving protection-motivated behaviors (PMBs) details the entire process of MIMIC modelling and provides a set of detailed guidelines for researchers to follow when developing new constructs modelled as MIMIC structures.

Keywords: Methodology, Formative Construct Validation, MIMIC Modelling, Covariance-based SEM, Protection-Motivated Behaviors

Suggested Citation

Posey, Clay and Roberts, Tom and Lowry, Paul Benjamin and Bennett, Becky, Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique: A Tutorial and an Annotated Example (September 10, 2014). Communications of the Association for Information Systems, vol. 36(11) , Available at SSRN: https://ssrn.com/abstract=2525093

Clay Posey

University of Central Florida ( email )

4000 Central Florida Blvd
Orlando, FL 32816-1400
United States

Tom Roberts

University of Kansas - School of Business ( email )

1300 Sunnyside Avenue
Lawrence, KS 66045
United States

Paul Benjamin Lowry (Contact Author)

Virginia Tech - Pamplin College of Business ( email )

1016 Pamplin Hall
Blacksburg, VA 24061
United States

Becky Bennett

Louisiana Tech University - Department of Management and Information Systems ( email )

United States

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